Consensus initiative-based cluster robot material proportion collection method and related device

Abstract

The application discloses a kind of cluster robot material proportion collection method based on consensus initiative and related device, S1, control robot random motion to any target node;S2, judge whether the target node exists required object type;If it does not exist, the signal that does not exist is stored in the short-term memory of corresponding robot, executes S1;If it exists, judge whether it needs to pick up this type of object;If it does not need to pick up, execute S1;If it needs to pick up, execute S3;S3, control corresponding robot picks up this type of object and stores object type, executes S4;S4, control robot continues random motion, if robot moves to any idle point, judge whether robot needs to release this type of object at idle point;If it does not need to release, execute S4;If it needs to release, execute S5;S5, control corresponding robot releases this type of object, executes S1, until the material collection area of scale set is formed, executes S6;S6, assess the proportion of each type of object in material collection area, determine the priority of each type of object collection according to the proportion of each type of object, the priority of each type of object collection is that the object with the lowest proportion has the highest priority, and each type of object is collected according to the priority, executes S1, until the proportion of each type of object in material collection area reaches the expected proportion.The application can flexibly adapt to the actual distribution of materials, improve the collection efficiency of materials.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cluster robot material collection, in particular to a cluster robot material proportion collection method based on consensus initiative and related device. BACKGROUND

[0002] "De novo construction" refers to the use of on-site materials for construction, the core of which is to make full use of on-site available materials for construction work. This method not only effectively reduces the dependence on external resources, but also reduces transportation costs and environmental impact, so it has advantages in remote areas, harsh weather conditions or extreme environments where resources are limited. In the implementation process of "de novo construction", material collection is the first step. Usually, concrete is used as the main building material, which needs to be mixed with multiple raw materials such as water, sand, and stones according to a specific ratio. These materials are often randomly distributed in the natural environment rather than in an orderly arrangement, making the material collection work more complicated.

[0003] The existing single robot shows high efficiency when performing material collection tasks in the face of evenly distributed materials, but its efficiency is greatly reduced when dealing with randomly distributed materials. The reason is that existing material collection techniques often rely on pre-set paths or fixed collection modes, making it difficult to adapt to the actual distribution of materials. SUMMARY

[0004] To solve the problems in the prior art, the present application provides a cluster robot material proportion collection method based on consensus initiative and related device, which can adapt to the actual distribution of materials and improve the collection efficiency of materials.

[0005] To solve the above technical problems, the present application realizes the following technical scheme:

[0006] According to the first aspect of the present application, a cluster robot material proportion collection method based on consensus initiative is provided, comprising:

[0007] S1, controlling the robot to move randomly to any target node;

[0008] S2, judging whether the target node exists the required object type; if not, storing the non-existent signal in the short-term memory of the corresponding robot, and executing S1; if yes, judging whether the type of object needs to be picked up; if not, executing S1; if yes, executing S3;

[0009] S3, controlling the corresponding robot to pick up the type of object and store the object type, and executing S4;

[0010] S4. Control the robot to continue moving randomly. If the robot moves to any idle point, determine whether the robot needs to release this type of object at the idle point. If it does not need to release, execute S4; if it needs to release, execute S5.

[0011] S5. Control the corresponding robot to release this type of object, execute S1, and execute S6 after a material collection area of ​​a set size is formed.

[0012] S6. Evaluate the proportion of each type of object in the material collection area, determine the collection priority of each type of object based on the proportion of each type of object, wherein the object with the lowest proportion has the highest priority, and collect each type of object according to the priority, and execute S1 until the proportion of each type of object in the material collection area reaches the desired proportion.

[0013] In one possible implementation of the first aspect, determining whether to pick up this type of object specifically involves:

[0014] Determine the probability of picking up this type of object at the current moment;

[0015] If the probability of picking up this type of object at the current moment reaches or exceeds the expected probability, then this type of object needs to be picked up; otherwise, it does not need to be picked up.

[0016] In one possible implementation of the first aspect, determining the probability of picking up this type of object at the current moment specifically involves:

[0017]

[0018] in, f is the probability of picking up this type of object at the current time t; k1 is a constant; α is a picking preference parameter, used to increase the picking probability of the highest priority object when collecting the highest priority object; i o (M) is a proportional estimate of the object type o in robot i's short-term memory M, defined as follows:

[0019]

[0020] Where s is the number of time steps the robot moves; Let O be the number of objects of type O encountered by robot i.

[0021] In one possible implementation of the first aspect, determining whether the robot needs to release this type of object at an idle point specifically involves:

[0022] Determine the probability of releasing this type of object at the current moment;

[0023] If the release probability of this type of object reaches or exceeds the expected release probability at the current moment, then this type of object needs to be released; otherwise, it does not need to be released.

[0024] In one possible implementation of the first aspect, determining the release probability of this type of object at the current moment specifically involves:

[0025]

[0026] in, f is the release probability of this type of object at the current time t; k2 is a constant; β is a release preference parameter, which increases the probability of releasing an object at this location if the current position is an empty point and the number of heterogeneous objects in neighboring nodes is greater than 2; i o (M) is a proportional estimate of the object type o in robot i's short-term memory M, defined as follows:

[0027]

[0028] Where s is the number of time steps the robot moves; Let O be the number of objects of type O encountered by robot i.

[0029] In one possible implementation of the first aspect, determining the collection priority of each type of object based on the proportion of each type of object specifically involves:

[0030] The robot compares the set of proportions for each type of object with the desired set of proportions, identifies object types whose quantities are far below the required proportions, and prioritizes collecting these object types. The priority function for object collection is as follows:

[0031]

[0032] in, It is a collection of proportions of various types of objects. It is the set of expected proportions, h k It is the difference between the current proportion and the ideal proportion of each type of object, argmin(h k ) is to find the largest h k k represents the type of object that is prioritized for collection.

[0033] In one possible implementation of the first aspect, determining whether the target node contains the required object type includes:

[0034] The robot acquires image information of the target node;

[0035] Determine whether the target node contains the required object type based on the image information of the target node.

[0036] According to a second aspect of the present invention, an apparatus is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the aforementioned consensus-based, proactive swarm robot material proportion collection method.

[0037] According to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned consensus-based, proactive cluster robot material proportion collection method.

[0038] According to a fourth aspect of the present invention, a computer program product is provided that, when executed by a processor, implements the aforementioned consensus-based, proactive swarm robot material proportion collection method.

[0039] Compared with the prior art, the present invention has at least the following beneficial effects:

[0040] This invention provides a consensus-based, proactive swarm robot material collection method. By controlling the robot to randomly move to any target node and determining whether the target node contains the required object type, this mechanism enables the robot to flexibly respond to the random distribution of materials. Compared with traditional methods that rely on preset paths or fixed collection patterns, this invention improves the adaptability of material collection. After confirming the presence of the required object type at the target node, the robot determines whether to pick up this type of object based on the current situation and performs the picking operation if necessary. Furthermore, the robot also determines whether to release objects at idle points based on release probabilities, further optimizing the material collection and release process. This method, based on real-time judgment and dynamic decision-making, enables the robot to efficiently complete material collection tasks in complex environments. Simultaneously, this invention evaluates the proportion of each type of object within the material collection area and determines the collection priority of each type of object based on the proportion. The priority adjustment mechanism ensures that the robot prioritizes collecting objects with the lowest proportion, thereby gradually bringing the proportion of each type of object within the material collection area to the desired proportion. This is crucial for in-situ construction processes that require mixing multiple raw materials in specific ratios. In summary, the consensus-based, proactive swarm robot material collection method proposed in this invention can flexibly adapt to the actual distribution of materials. In the in-situ construction material collection process, the swarm robots significantly improve collection speed and task completion efficiency through multi-robot collaborative operation, while also reducing dependence on external resources and transportation costs. It also has good adaptability and robustness, can flexibly respond to task requirements and environmental changes, and has good scalability, ensuring task continuity and efficiency.

[0041] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the drawings used in the description of the specific embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1 This is a flowchart of a consensus-based, proactive method for collecting materials in a cluster of robots, according to an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] This invention uses isomorphic swarm robots. The robots can sense the type and location of materials, and their heads are equipped with materials-picking devices, enabling them to pick up and release materials.

[0046] like Figure 1 As shown, this invention provides a consensus-based, proactive method for collecting materials using swarm robots, primarily to address the problem of low material collection efficiency in randomly distributed material environments in existing technologies. The collection method specifically includes the following steps:

[0047] S1. Control the robot to move randomly to any target node.

[0048] Specifically, at the start of the material collection task, all swarm robots are initialized and randomly distributed within the work area. Using a pre-defined random motion algorithm, such as a random walk algorithm or the Levy flight algorithm, each robot independently selects its next target node to move to, ensuring that the robot can cover the entire work area and increasing the probability of encountering randomly distributed materials.

[0049] S2. Determine if the target node contains the required object type. If it does not, store the non-existence signal in the corresponding robot's short-term memory and execute S1. If it does, determine whether this type of object needs to be picked up. If it does not need to be picked up, execute S1. If it needs to be picked up, execute S3.

[0050] Specifically, once the robot reaches the target node, it scans the surrounding environment using integrated sensors (such as cameras, infrared sensors, or lidar) to identify the presence of the required type of object (such as sand, stones, or water). The sensor data is processed in real time to determine whether the target node contains a specific type of material.

[0051] If the desired object type does not exist, the robot stores a "not found" signal in its short-term memory and returns to execute S1, i.e., reselects and moves to another random target node. For example, the "not found" signal can be set to 0.

[0052] If the required object type exists, the robot will further determine whether it needs to pick up such an object.

[0053] S3. Control the corresponding robot to pick up this type of object and store the object type, then execute S4.

[0054] Specifically, when the robot determines that it needs to pick up a certain type of object, it will use its manipulator (such as a robotic arm or suction cup) to pick up the object and store it in a built-in container. At the same time, the robot stores the object's type information in its memory.

[0055] S4. Control the robot to continue moving randomly. If the robot moves to any idle point, determine whether the robot needs to release this type of object at the idle point. If it does not need to be released, execute S4. If it needs to be released, execute S5.

[0056] Specifically, after picking up an object, the robot continues to move to a new target node according to a random motion algorithm in order to find more material or an empty point to release the material.

[0057] S5. Control the corresponding robot to release this type of object, execute S1, and continue until a material collection area of ​​a set size is formed, then execute S6.

[0058] Specifically, when the robot moves to a preset idle point and decides to release the material, the robot will release the object it is carrying. It should be understood that the release operation is also performed by the robot's operating mechanism.

[0059] It should be noted that a material collection area of ​​a set size refers to a material collection area that is formed when objects are released, piled up, or accumulated to a certain extent.

[0060] S6. Evaluate the proportion of each type of object in the material collection area, determine the collection priority of each type of object based on the proportion of each type of object, wherein the object with the lowest proportion has the highest priority, and collect each type of object according to the priority, and execute S1 until the proportion of each type of object in the material collection area reaches the desired proportion.

[0061] Specifically, as the material collection process progresses, the swarm of robots periodically assesses the proportion of each type of object within the current material collection area. It should be understood that this assessment is achieved through communication between the robots; each robot shares information about the types and quantities of materials it has collected, forming a global material distribution map. Based on the assessment results, the collection priority for each type of object is determined, with the object having the lowest proportion having the highest priority. This strategy ensures that the material collection process gradually approaches the desired material proportion. Finally, the robots continue executing steps S1 to S5 according to the new priority rules until the proportion of each type of object within the material collection area reaches or approaches the desired proportion.

[0062] The consensus-based, proactive swarm robot material proportion collection method provided in this embodiment can flexibly adapt to the random distribution of materials, improve material collection efficiency, and ensure that the final collected materials meet the predetermined proportion requirements. This method has significant advantages in remote areas, harsh climate conditions, or resource-constrained extreme environments.

[0063] In one possible implementation, determining whether this type of object needs to be picked up may specifically include:

[0064] First, determine the probability of picking up this type of object at the current moment;

[0065] Finally, if the probability of picking up this type of object at the current moment reaches or exceeds the expected probability of picking up, then this type of object needs to be picked up; otherwise, it does not need to be picked up.

[0066] In other words, a probability-based decision-making mechanism is used to determine whether to pick up a certain type of object. This picking probability reflects the rationality and necessity of picking up that type of object in the current context. If the picking probability of this type of object at the current moment reaches or exceeds the expected picking probability, then the robot needs to pick up this type of object. If the picking probability of this type of object at the current moment is lower than the expected picking probability, then the robot does not need to pick up this type of object and directly returns to execute S1, that is, reselects and moves to another random target node.

[0067] It should be noted that the expected picking probability can be preset according to the actual situation, such as setting it to a certain threshold.

[0068] Preferably, determining the probability of picking up this type of object at the current moment can be done as follows:

[0069]

[0070] in, f is the probability of picking up this type of object at the current time t; k1 is a constant; α is a picking preference parameter, used to increase the picking probability of the highest priority object when collecting the highest priority object; i o (M) is a proportional estimate of the object type o in robot i's short-term memory M, defined as follows:

[0071]

[0072] Where s is the number of time steps the robot moves; Let O be the number of objects of type O encountered by robot i.

[0073] In one possible implementation, determining whether the robot needs to release this type of object at an idle point can be done as follows:

[0074] First, determine the probability of releasing this type of object at the current moment;

[0075] Finally, if the release probability of this type of object reaches or exceeds the expected release probability at the current moment, then this type of object needs to be released; otherwise, it does not need to be released.

[0076] In other words, after the robot reaches an idle point, it also uses a decision-making mechanism based on release probability to determine whether it needs to release a certain type of object it is carrying. This release probability reflects whether releasing the object is appropriate and necessary. If the release probability at the current moment reaches or exceeds the expected release probability, then the robot will release this type of object at the current idle point; if the release probability at the current moment is lower than the expected release probability, then the robot will continue to carry this type of object and look for a more suitable time or place to release it.

[0077] Similarly, the expected release probability can be preset according to the actual situation.

[0078] Preferably, the determination of the release probability of this type of object at the current moment is as follows:

[0079]

[0080] in, f is the release probability of this type of object at the current time t; k2 is a constant; β is a release preference parameter, which increases the probability of releasing an object at this location if the current position is an empty point and the number of heterogeneous objects in neighboring nodes is greater than 2; i o(M) is a proportional estimate of the object type o in robot i's short-term memory M, defined as follows:

[0081]

[0082] Where s is the number of time steps the robot moves; Let O be the number of objects of type O encountered by robot i.

[0083] In one possible implementation, determining the collection priority of each type of object based on the proportion of each type of object may specifically include:

[0084] The robot compares the set of proportions for each type of object with the desired set of proportions, identifies object types whose quantities are far below the required proportions, and prioritizes collecting these object types. The priority function for object collection is as follows:

[0085]

[0086] in, It is a collection of proportions of various types of objects. It is the set of expected proportions, h k It is the difference between the current proportion and the ideal proportion of each type of object, argmin(h k ) is to find the largest h k k represents the type of object that is prioritized for collection.

[0087] In one possible implementation, determining whether the target node contains the required object type can specifically be:

[0088] The robot acquires image information of the target node; based on the image information of the target node, it determines whether the target node contains the required object type.

[0089] Specifically, the robot uses its integrated camera or other image acquisition device to photograph or scan the target node, acquiring image information of the target node. This image information can be a still image, video frame, or dynamic video stream. After acquiring the image information, the robot preprocesses it to improve the accuracy of subsequent image recognition. For example, preprocessing steps may include image denoising, image enhancement, image scaling, and image cropping. Preprocessing operations can eliminate interfering factors in the image, such as changes in lighting, shadows, and occlusions, thereby improving image quality. The preprocessed image information is then input into the image recognition module. It should be noted that the image recognition module uses a pre-trained image recognition network (such as a convolutional neural network, support vector machine, decision tree, etc.) to analyze and recognize the image, determining whether the target node contains the required object type.

[0090] For example, the robot has perception capabilities, enabling it to identify objects and other robots. The robot acquires information about the type and location of targets to be collected using its onboard camera (binocular camera); based on this information, it determines whether the target node contains the desired object type. The robot has a grasping device to pick up or release targets. The robot also has communication capabilities; when one robot senses another, it receives the information perceived by the other robot and simultaneously feeds back its own perceived information, including the types and locations of all targets perceived by that individual robot. A host computer evaluates the proportion of each type of object within the material collection area and sends the collected objects to the robots according to their priority.

[0091] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a consensus-based, proactive swarm robot material proportion collection method.

[0092] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the consensus-based, proactive swarm robot material proportion collection method described in the above embodiments.

[0093] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0095] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0096] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] This invention also provides a computer program product for executing any of the consensus-based, proactive swarm robot material proportion collection methods described above. Since the computer program product provided by this invention belongs to the same inventive concept as the consensus-based, proactive swarm robot material proportion collection method described above, it possesses all the advantages of the consensus-based, proactive swarm robot material proportion collection method described above. Therefore, the beneficial effects of the computer program product provided by this invention will not be elaborated upon here.

[0098] In this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0099] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for proportional material collection using swarm robots based on consensus-driven initiative, characterized in that, include: S1. Control the robot to move randomly to any target node; S2. Determine if the target node contains the required object type; If it does not exist, store the absence signal in the short-term memory of the corresponding robot and execute S1; If it exists, determine whether this type of object needs to be picked up; if it does not need to be picked up, execute S1; if it needs to be picked up, execute S3. S3. Control the corresponding robot to pick up this type of object and store the object type, then execute S4; S4. Control the robot to continue moving randomly. If the robot moves to any idle point, determine whether the robot needs to release this type of object at the idle point. If it does not need to release, execute S4; if it needs to release, execute S5. S5. Control the corresponding robot to release this type of object, execute S1, and execute S6 after a material collection area of ​​a set size is formed. S6. Evaluate the proportion of each type of object in the material collection area, determine the collection priority of each type of object based on the proportion of each type of object, wherein the object with the lowest proportion has the highest priority, and collect each type of object according to the priority, and execute S1 until the proportion of each type of object in the material collection area reaches the desired proportion.

2. The method for proportional material collection by a cluster robot based on consensus initiative according to claim 1, characterized in that, The determination of whether this type of object needs to be picked up specifically involves: Determine the probability of picking up this type of object at the current moment; If the probability of picking up this type of object at the current moment reaches or exceeds the expected probability, then this type of object needs to be picked up; otherwise, it does not need to be picked up.

3. The method for proportional material collection by a cluster robot based on consensus initiative according to claim 2, characterized in that, The determination of the probability of picking up this type of object at the current moment is specifically as follows: in, f is the probability of picking up this type of object at the current time t; k1 is a constant; α is a picking preference parameter, used to increase the picking probability of the highest priority object when collecting the highest priority object; i o (M) is a proportional estimate of the object type o in robot i's short-term memory M, defined as follows: Where s is the number of time steps the robot moves; Let O be the number of objects of type O encountered by robot i.

4. The method for proportional material collection by a cluster robot based on consensus initiative according to claim 1, characterized in that, The determination of whether the robot needs to release this type of object at an idle point specifically involves: Determine the probability of releasing this type of object at the current moment; If the release probability of this type of object reaches or exceeds the expected release probability at the current moment, then this type of object needs to be released; otherwise, it does not need to be released.

5. The method for proportional material collection by a cluster robot based on consensus initiative according to claim 4, characterized in that, The determination of the release probability of this type of object at the current moment is specifically as follows: in, f is the release probability of this type of object at the current time t; k2 is a constant; β is a release preference parameter, which increases the probability of releasing an object at this location if the current position is an empty point and the number of heterogeneous objects in neighboring nodes is greater than 2; i o (M) is a proportional estimate of the object type o in robot i's short-term memory M, defined as follows: Where s is the number of time steps the robot moves; Let O be the number of objects of type O encountered by robot i.

6. The method for proportional material collection by a cluster robot based on consensus initiative according to claim 1, characterized in that, The method of determining the collection priority of each type of object based on the proportion of each type of object is as follows: The robot compares the set of proportions for each type of object with the desired set of proportions, identifies object types whose quantities are far below the required proportions, and prioritizes collecting these object types. The priority function for object collection is as follows: in, It is a collection of proportions of various types of objects. It is the set of expected proportions, h k It is the difference between the current proportion and the ideal proportion of each type of object, argmin(h k ) is to find the largest h k k represents the type of object that is prioritized for collection.

7. The method for proportional material collection by a cluster robot based on consensus initiative according to claim 1, characterized in that, The determination of whether the target node contains the required object type includes: The robot acquires image information of the target node; Determine whether the target node contains the required object type based on the image information of the target node.

8. An apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a consensus-based, proactive method for collecting materials in a cluster of robots as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a consensus-based, proactive method for collecting materials in a cluster of robots as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, When the computer program product is executed by the processor, it implements a consensus-based, proactive swarm robot material proportion collection method as described in any one of claims 1 to 7.

Images